جستجوی استاد راهنمای دانشگاهی

• Stochastic programming
• Two-phase and multi-phase models
• Scenario generation and reduction
• +۹ مورد دیگر

Trine Krogh Boomsma is a Professor in the Department of Insurance and Economics at the University of Copenhagen's Department of Mathematical Sciences. Her research focuses on optimization under uncertainty with significant applications in energy systems, particularly electricity markets, renewable energy investments, and power system planning. PhD in Mathematics-Economics, Aarhus University (2003-2007) Visiting PhD at University of Duisburg-Essen (2004) Academic career includes positions at Risø National Laboratory for Renewable Energy and Imperial College London Her work spans stochastic programming, real options analysis, and dynamic programming to address energy sector challenges. Key areas include support schemes for renewables, market risk modeling, and operational optimization of hybrid conventional-renewable systems. Recent research explores policy impacts on investment decisions and advanced scenario generation techniques. Major publications (2012-2020) cover renewable energy policy frameworks, power plant valuation models, and sequential market bidding strategies. These works emphasize electricity market dynamics, investment risk quantification, and robust planning under uncertainty. She teaches linear programming, integer programming, and stochastic programming applications in operational analysis, contributing to energy economics education at the department.

Francisco Camara Pereiraمشاهده پروفایل

استاد

• Intelligent Transportation Systems
• Machine Learning
• Active Learning
• +۳ مورد دیگر

Francisco Camara Pereira is a Professor and Head of Section at the Department of Technology, Management and Economics at the Technical University of Denmark (DTU). His research focuses on Intelligent Transportation Systems, Machine Learning, and Data-Driven Decision-Making in transportation contexts. He actively contributes to advancing transportation science through interdisciplinary approaches combining simulation, optimization, and AI techniques. His work addresses challenges in public transport analysis, charging infrastructure planning, and multimodal demand prediction. Recent projects include developing graph-based optimization methods for electric vehicle networks and causal discovery frameworks for transportation systems. He supervises multiple PhD students in areas like federated learning for cyclist safety, causal graph neural networks, and socially aware AI models. Key contributions include publications on smart card data analysis for travel surveys, stochastic infrastructure expansion models, and transfer learning for bike-share systems. His research aligns with UN Sustainable Development Goals related to sustainable cities and innovation. Dr. Pereira collaborates internationally on transportation policy and infrastructure projects. His lab focuses on translating theoretical advancements into practical solutions for urban mobility challenges.

• Optimization under Uncertainty
• Stochastic Programming
• Risk Analysis
• +۶ مورد دیگر

Giovanni Pantuso is an Associate Professor at the Department of Mathematical Sciences, University of Copenhagen, specializing in stochastic programming and optimization under uncertainty . His work bridges mathematical methods with practical applications in transportation, logistics, and production planning. Education : PhD in Operations Analysis from the Norwegian University of Science and Technology (Feb 2014) Research Focus : Developing mathematical frameworks for decision-making under risk, with applications to maritime fleet renewal, car-sharing systems, and ride-sharing logistics. Teaching : Courses in Advanced Operations Research: Stochastic Programming, Risk Optimization, and Introduction to Numerical Analysis. His methodological contributions include novel algorithms for stochastic programming and decomposition methods, while applied work spans electric car-sharing systems, first-mile transportation challenges, and production planning under uncertainty. Current research explores dynamic fleet management and cost-service tradeoffs in shared mobility.

Wiebke Meesenburgمشاهده پروفایل

استادیار

• Thermal Energy
• Heat Pump Engineering
• District Heating
• +۷ مورد دیگر

Wiebke Meesenburg is an Assistant Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU), specializing in Thermal Energy. She is actively involved in research on large-scale heat pump systems, district heating integration, and digital twin applications for energy optimization. Her research focuses on sustainable thermal energy systems, particularly the design, monitoring, and optimization of heat pumps in district heating networks. Key areas include dynamic modeling, real-time adaptation, fouling mitigation, and the integration of renewable energy sources. She contributes to advancing energy efficiency and sustainability in urban infrastructure. The recent publications highlight a strong trend toward digitalization and optimization of thermal systems, with an emphasis on model-based monitoring, digital twins, and operation scheduling using advanced algorithms. Her work bridges mechanical engineering, energy systems, and computational modeling to improve system performance and reliability. She has supervised PhD research and contributed to major projects such as the implementation of digital twins for heat pump systems and EnergyLab Nordhavn. Collaborations involve key figures in energy research at DTU, including Professor Brian Elmegaard. While no formal awards are listed, her active participation in conferences and project leadership demonstrates recognition in her field. Wiebke Meesenburg has been involved in organizing and presenting at international events, including the 35th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems and workshops on Modelica and flexible heat supply. Her work is embedded in interdisciplinary teams focused on future energy infrastructures and smart urban energy systems.

• Programming Languages
• Functional Programming
• Array Programming
• +۴ مورد دیگر

Troels Henriksen is an Assistant Professor on Tenure Track at the Department of Computer Science (DIKU) at the University of Copenhagen, where he is affiliated with the Programming Languages and Theory of Computation research section. His research focuses on programming languages, particularly functional array programming languages, compiler design, and parallel computing. He maintains an active research profile with numerous publications in top-tier programming language conferences. Dr. Henriksen's research interests center around programming language theory and implementation, with particular emphasis on functional array programming languages. His work bridges theoretical foundations with practical high-performance computing applications. His research spans type systems, compiler optimizations, parallelism, and memory management in the context of array programming languages, contributing to both academic knowledge and practical language implementations. His recent publications reveal a strong focus on array programming language design and implementation. There is a clear trend toward optimizing functional array languages for high-performance computing environments, with significant work on fusion optimizations, parallelism, and memory management. His research often intersects with practical applications in scientific computing and machine learning, particularly through work on automatic differentiation for array languages. Dr. Henriksen is actively involved in the programming languages research community, regularly publishing in prestigious venues such as the ACM SIGPLAN conferences. His collaborations span multiple institutions, indicating an active research network in the programming languages field.

Konstantin Pavlikovمشاهده پروفایل

دانشیار

• Operations Research
• Stochastic Programming
• Integer Programming
• +۳ مورد دیگر

Konstantin Pavlikov is an Associate Professor at the Department of Business & Management (DBM) under Strategic Organization Design (SOD) at the University of Southern Denmark. His research focuses on Operations Research, Integer Programming, and Stochastic Programming , with particular emphasis on vehicle routing optimization and network flow modeling. Education : PhD in Operations Research (University of Florida, 2014), MSc in Applied Mathematics (Moscow State University, 2007) His work spans combinatorial optimization and network interdiction problems , developing exact and approximate solution algorithms for complex logistics challenges. Recent publications analyze heterogeneous vehicle routing capacity inequalities and two-commodity flow formulations for routing problems. Scientific contributions have been recognized with the Best Reviewer Award (2019). He actively reviews for journals like Computational Management Science and supervises academic works through examination roles.

Jakob Lykke Andersenمشاهده پروفایل

دانشیار

• Algorithms
• Graph Transformation
• Chemical Reaction Networks
• +۷ مورد دیگر

Jakob Lykke Andersen is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in algorithms with applications in cheminformatics and complex systems. He also holds a former external appointment as a Research Fellow at the Tokyo Institute of Technology (2015–2017). Research Interests: His work lies at the intersection of computer science and theoretical chemistry, focusing on algorithmic methods for analyzing chemical reaction networks. He employs hypergraphs, mixed-integer linear programming, and probabilistic models to study metabolic pathways, reaction databases, and prebiotic systems. His research emphasizes computational efficiency, formal modeling, and software implementation. Publication Trends: His recent publications (2019–2025) show a consistent focus on graph-based modeling of chemical systems, rule extraction from reaction databases, thermodynamic feasibility, and stochastic analysis. These works appear in high-impact journals in cheminformatics, bioinformatics, and complex systems, reflecting strong interdisciplinary collaboration. Scientific Contributions: While no specific awards are listed, his sustained research output and leadership in funded projects highlight significant contributions to algorithmic cheminformatics. Grants and Projects: He is actively involved in two major ongoing research projects: (1) Software Infrastructures for Teaching at Scale (funded by Innovation Fund Denmark, 2022–2025), and (2) DIREC (Danish Research Center for Digital Economy, 2020–2025), indicating active engagement in both educational technology and core computational research. Advising and Outreach: While no students are listed, he participates in academic advising through project supervision. He contributes to public discourse through media appearances on topics such as mathematics in health (e.g., intestinal system modeling in obesity) and educational well-being. Labs and Teams: He collaborates with interdisciplinary teams, including researchers from bioinformatics, chemistry, and computer science, particularly through projects involving Merkle, Flamm, Fagerberg, and Stadler. His work is associated with algorithmic cheminformatics and software framework development groups at SDU.

Hao Liمشاهده پروفایل

پژوهشگر ارشد

• Topology Optimization
• Mechanical Engineering
• Thermal Systems
• +۲ مورد دیگر

Hao Li is a MSCA Postdoc Fellow and Visiting Scholar at LIP6 , affiliated with the University of Southern Denmark in the Department of Mechanical Engineering . His research focuses on advanced computational methods for topology optimization in thermal, fluid, and structural engineering systems. Education: Not explicitly stated in the provided text. Current Projects: Leading EU-funded research on heat exchanger design using multiscale models and machine learning. Dr. Li's work spans multiscale topology optimization, level-set methods, and fluid-structure interaction, with applications in microchannel cooling, compliant mechanisms, and biodegradable composites. His recent publications highlight advancements in 3D conjugate heat transfer, adaptive meshing, and eigenfrequency maximization. The trends in his research output (2017–2025) emphasize thermal-fluid systems , high-resolution structural optimization , and manufacturable composite designs . Notable subfields include triply periodic minimal surfaces for cooling channels, nonlinear buckling analysis, and phasor-based dehomogenization techniques. Teaching & Supervision: Currently supervising projects on topology optimization frameworks for heat sinks and high heat flux cooling. His past projects (2018–2023) include research on piezoelectric transducers and thermal-fluid system design. Labs & Collaborations: Collaborates with institutions in Japan and France, focusing on experimental validation and industrial applications. His network includes partnerships with researchers in structural mechanics, computational fluid dynamics, and additive manufacturing.

Magnus Madsenمشاهده پروفایل

دانشیار

• Programming Languages
• Compilers
• Type Systems
• +۴ مورد دیگر

Magnus Madsen is an Associate Professor at the Department of Computer Science, Aarhus University. He specializes in programming language design, compilers, and type and effect systems, and is the lead developer of the Flix programming language. His research focuses on advancing static analysis techniques and declarative language constructs for effectful and data-driven programming. Research Interests : - Programming language design - Type systems and effect systems - Static program analysis - Datalog and declarative programming - Compiler optimization and implementation Awards & Grants : - 2023: Sapere Aude Grant (Independent Research Fund Denmark) - 2022: Dahl-Nygaard (Junior) Prize (ECOOP) - 2022: STEM Grant (Stibo Foundation) - 2021: Amazon Research Award (collaboration with Jaco van de Pol) - 2020: DFF Project One (Independent Research Fund Denmark) Advising & Collaboration : - Supervises four current PhD students (listed above). - Active in academic service: PC member for ECOOP, OOPSLA, PLDI, and other conferences. - Collaborates with industry (e.g., Google, Systematic) and academia on tool development and language research. Labs & Projects : - Core contributor to the Flix programming language and the CASA (Center for Advanced Software Analysis) initiative. - Involved in interdisciplinary projects combining declarative programming with domain-specific applications.

Bernd Dammannمشاهده پروفایل

دانشیار

• High-Performance Computing
• Scientific GPU Computing
• Parallel Programming
• +۳ مورد دیگر

Bernd Dammann is an Associate Professor in Scientific Computing at the Technical University of Denmark (DTU) , Department of Applied Mathematics and Computer Science. In parallel he serves as Scientific Lead & Architect in the DTU Computing Center (DCC) and co-founded DTU’s GPUlab in 2007, giving him dual roles in both academic research and institutional HPC services. Education Dipl.Phys. in Statistical Physics, University of Mainz, 1992 PhD in Physical Chemistry, DTU, 1996 Research interests Over almost three decades Dammann has focused on computational methods for large-scale scientific problems. His core expertise lies in: High-Performance Computing and parallel programming paradigms (OpenMP, MPI, GPU) Scientific GPU computing and many-core architectures Application tuning and performance optimization for heterogeneous systems Uncertainty quantification, Monte-Carlo methods, and real-time control applications in biomedicine (diabetes technology) Ultrasound beamforming and advanced imaging techniques Publication trends His 34 publications (1996-2025) trace a clear evolution from theoretical physics and surface-science simulations to GPU-accelerated engineering and biomedical systems. Recent work clusters around real-time biomedical control (artificial pancreas, virtual clinical trials) and high-resolution ultrasound imaging, all leveraging GPU acceleration and high-performance computing to achieve real-time performance. Scientific awards & recognition Contributor to 1 patent family and widely downloaded technical reports (>10 k downloads for OpenCL tutorial) Consistently high citation counts on key optimization and control papers (e.g., 93 citations for 2014 Management Science article) Teaching, supervision & grants He teaches or co-teaches multiple DTU courses: High-Performance Computing , Large-scale Computations , Python & HPC , Mathematical Software , and HPC with FORTRAN . While individual students are not named, his role in GPUlab and DCC implies ongoing PhD and MSc supervision, and his projects list six active collaborations including EU and national HPC infrastructure grants. Labs & teams Dammann co-founded GPUlab (2007) and presently leads the HPC architecture team in the DTU Computing Center , a central facility supplying cluster resources to the entire university.

Lars Rohwedderمشاهده پروفایل

دانشیار

• Algorithms
• Scheduling Theory
• Integer Programming
• +۳ مورد دیگر

Lars Rohwedder is an Associate Professor in Algorithms at the Department of Mathematics and Computer Science, University of Southern Denmark (SDU). His research focuses on theoretical computer science, particularly in the development of efficient algorithms for scheduling and optimization problems. Research Interests: Algorithms, Scheduling Theory, Integer Programming, Approximation Algorithms, Operations Research, Theoretical Computer Science. Recent work includes advancements in coflow scheduling approximation, makespan minimization algorithms, and fine-grained complexity analysis. Rohwedder is involved in the NWO-funded project The Twilight Zone of Efficiency: Optimality of Quasi-Polynomial Time Algorithms , exploring computational efficiency boundaries.

Kent Andersenمشاهده پروفایل

دانشیار

• Operations Research
• Mathematical optimization
• Mixed Integer Optimization
• +۱ مورد دیگر

Kent Andersen is an Associate Professor at the Department of Mathematics, Aarhus University. His expertise spans Operations Research, Mathematical optimization, Mixed Integer Optimization, and Vessel Stowage Planning, focusing on advanced algorithm development for logistics and shipping efficiency. He has been actively involved in industrial research, notably leading the REALCAP project (2016–2018) on next-generation capacity models for container ships. His work bridges theoretical optimization with practical maritime logistics challenges. Publications in journals like Management Science and Mathematical Programming highlight his contributions to mixed integer programming and intersection cuts. While specific awards are not listed, his research output and industry collaborations underscore significant academic impact.

Andreas Kloseمشاهده پروفایل

دانشیار

• Operations Research
• Combinatorial Optimization
• Integer Programming
• +۳ مورد دیگر

Andreas Klose is an Associate Professor at the Department of Mathematics, Aarhus University, Faculty of Science. His work focuses on combinatorial optimization and integer programming, particularly for logistics applications. Active in exact solution approaches for bi-objective and uncertain facility location problems Develops metaheuristics for fixed charge transportation challenges Teaches master courses on location planning, vehicle routing, and optimization methods Research Trends: Recent publications emphasize facility location under uncertainty, exact algorithms for capacitated problems, and metaheuristic approaches. Topics span both theoretical and applied operations research. Teaching: Offers courses on Multiple Criteria Optimization , Location Planning , Vehicle Routing , and Metaheuristics , alternating between fall and spring semesters. Collaborations: Frequently collaborates with researchers like Simon Gadegaard and Kim Jörnsten on facility location and transportation problem frameworks.

Shuting Liمشاهده پروفایل

پژوهشگر

• Microgrids
• Energy Engineering
• Virtual Synchronous Generators
• +۵ مورد دیگر

Dr. Shuting Li is a Postdoctoral Researcher at Aalborg University's Faculty of Engineering and Science within the Applied Power Electronic Systems group. Her research focuses on advanced control strategies for renewable energy integration, particularly in microgrid applications and virtual synchronous generator technologies. Her research interests span Microgrids , Virtual Synchronous Generators , Harmonics Control , and Wind Power Forecasting . She develops innovative control architectures addressing harmonic mitigation, stability enhancement, and energy management in systems with high renewable penetration. Her work bridges theoretical control design with practical implementation challenges in modern power systems. Dr. Li's publication record demonstrates consistent output with 15 research items between 2020-2024, including 7 publications in 2024 alone. Her work shows strong focus on harmonic control ( 46% fingerprint match ), virtual synchronous generators ( 46% ), and energy management systems ( 46% ), with significant contributions to wind power integration ( 30% ) and regenerative braking applications ( 30% ). She has secured research funding through the Harmonic Control Architectures for Virtual Synchronous Generator-based Distributed Generation Systems project (2021-2024), supervised by Professors Guerrero and Vasquez. This PhD project established her expertise in harmonic mitigation for distributed generation systems. Her laboratory work centers on the Applied Power Electronic Systems facility at Pontoppidanstræde 111, where she develops and tests control algorithms for microgrid applications using advanced simulation and hardware-in-loop platforms.

Thomas Jacob Riis Stidsenمشاهده پروفایل

دانشیار

• Operations Research
• Mathematical Modelling
• Optimization Algorithms
• +۲ مورد دیگر

Dr. Thomas Jacob Riis Stidsen is an Associate Professor in the Department of Technology, Management and Economics at the Technical University of Denmark. His research specializes in operations research methodologies applied to industrial planning problems, including mathematical modeling, decomposition algorithms, and metaheuristic optimization techniques. His expertise spans operations research, mathematical modeling, optimization algorithms, educational timetabling, and telecommunications optimization. He develops practical solutions for scheduling, resource allocation, and planning systems across education, healthcare, and production sectors. Research outputs from 2022-2025 focus heavily on educational timetabling optimization, featuring novel algorithms for school and examination scheduling. Recent publications demonstrate strong emphasis on mixed integer programming and metaheuristic approaches to complex scheduling challenges in real-world contexts. Stidsen has received recognition for his contributions to timetabling research, including winning the International Timetabling Competition 2019. His work bridges theoretical operations research with practical implementation in educational and healthcare systems.